A generalized procedure for joint monitoring and probabilistic quantification of extreme climate events at regional level

Batool, Aamina; Ali, Zulfiqar; Mohsin, Muhammad; Shakeel, Muhammad

doi:10.1007/s10661-023-11717-5

Cited by 8 publications

(1 citation statement)

References 46 publications

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“…However, 32 , 33 utilized the same study area for studying drought. Whereas, the standardization procedure is selected, based on 34 research. Moreover, 24 also studied the long-term statistic of precipitation by using Steady state probabilities.…”

Section: Methodsmentioning

confidence: 99%

Development of maximum relevant prior feature ensemble (MRPFE) index to characterize future drought using global climate models

Gul,

Qamar,

Yousaf

et al. 2024

Sci Rep

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Drought is one of the foremost outcomes of global warming and global climate change. It is a serious threat to humans and other living beings. To reduce the adverse impact of drought, mitigation strategies as well as sound projections of extreme events are essential. This research aims to strengthen the robustness of anticipated twenty-first century drought by combining different Global Climate Models (GCMs). In this article, we develop a new drought index, named Maximum Relevant Prior Feature Ensemble index that is based on the newly proposed weighting scheme, called weighted ensemble (WE). In the application, this study considers 32 randomly scattered grid points within the Tibetan Plateau region and 18 GCMs of Coupled Model Intercomparison Project Phase 6 (CMIP6) of precipitation. In this study, the comparative inferences of the WE scheme are made with the traditional simple model averaging (SMA). To investigate the trend and long-term probability of various classes, this research employs Markov chain steady states probability, Mann–Kendall trend test, and Sen’s Slope estimator. The outcomes of this research are twofold. Firstly, the comparative inference shows that the proposed weighting scheme has greater efficiency than SMA to conflate GCMs. Secondly, the research indicates that the Tibetan Plateau is projected to experience “moderate drought (MD)” in the twenty-first century.

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Section: Methodsmentioning

confidence: 99%

Development of maximum relevant prior feature ensemble (MRPFE) index to characterize future drought using global climate models

Gul,

Qamar,

Yousaf

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

Improving drought monitoring using climate models with bias‐corrected under Gaussian mixture probability models

Naz,

Ali,

Kartal

et al. 2024

Intl Journal of Climatology

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Global climate models (GCMs) are extensively used to calculate standardized drought indices. However, inaccuracies in GCM simulations and uncertainties inherent in the standardization methodology limit the precision of drought evaluations. The objective of this research is to remove bias in GCMs for improving drought monitoring and assessment. Consequently, this article proposes a new framework for drought index under the ensemble of GCMs—Multi‐Model Quantile Mapped Standardized Precipitation Index (MMQMSPI). In accordance of Standardized Precipitation Index (SPI), the second stage derives a new index by assessing the feasibility of parametric and nonparametric models during standardization. In the application, we used 18 GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) data of precipitation across 32 grid points within the Tibetan Plateau region. The comparative findings reveal that the integration of KCGMD is the most suitable choice compared to other best‐fitted univariate distributions in both features of the proposed framework. In this research, we assess the implications of evaluating future patterns of drought for the years 2015–2100 using seven different time periods and three different future scenarios. Temporal behavior clearly shows monthly variations in the pattern of MMQMSPI, and these variations differ on each time scale, but a drastic change can be seen over the long term, i.e., extreme dry and wet conditions, with a higher probability in all scenarios.

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A novel self-adjusting weight approximation procedure to minimize non-identical seasonal effects in multimodel ensemble for accurate twenty-first century drought assessment

Naz,

Ali

2024

Stoch Environ Res Risk Assess

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A generalized procedure for joint monitoring and probabilistic quantification of extreme climate events at regional level

Cited by 8 publications

References 46 publications

Development of maximum relevant prior feature ensemble (MRPFE) index to characterize future drought using global climate models

Development of maximum relevant prior feature ensemble (MRPFE) index to characterize future drought using global climate models

Improving drought monitoring using climate models with bias‐corrected under Gaussian mixture probability models

A novel self-adjusting weight approximation procedure to minimize non-identical seasonal effects in multimodel ensemble for accurate twenty-first century drought assessment

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